10 results about "List mode" patented technology

The application particularly relates to a kind of asset performance analysis methods based on self-defined algorithm.The asset performance analysis method based on self-defined algorithm is through interface management algorithm factor, sets the mapping relationship of algorithm factor and collected data, and collects and stores algorithm factor data, presents algorithm factor index data in list mode;Algorithm formula is self-defined and set and legality check;Set early warning rule;If the data of current granularity has completed data collection in this period, then trigger algorithm scheduling;Algorithm calculation is carried out based on algorithm factor data;Automatic trigger early warning analysis, generate early warning message according to early warning rule, notify relevant person in charge.The asset performance analysis method based on self-defined algorithm solves the problem that analysis algorithm basic data cannot be quickly expanded, so that the expansion of basic data can be directly applied to algorithm, which can quickly realize the expansion and adjustment of algorithm, and also provides an effective means for the optimization and fine adjustment of algorithm.

The invention discloses a browser-based data security processing method and system, and belongs to the technical field of data processing. According to the method, a hook program is implanted into a browser rendering pipeline; detecting data to be transmitted through a data transmission operation performed by the browser through the hook program; determining a control used for transmitting the data in a page displayed by the browser through the hook program, and forbidding a function of the control through the hook program; detecting the data, and keeping the data transmission function of the control to be disabled under the condition that the data is determined to comprise the sensitive content; and under the condition that the data does not comprise the sensitive content, recovering the data transmission function of the control. According to the method and the device, the problem caused by limiting the use lock of the user on the browser in a black and white list mode in order to ensure the data security in the prior art is solved, so that the data security is ensured to a certain extent while the user freely uses the browser.

Embodiments of the present disclosure provide a solution for video processing. A method for video processing is proposed. In the method, for a conversion between a current video block of a video and a bitstream of the video, whether a cross component prediction (CCP) candidate list mode is applicable to the current video block is determined based on coding information of at least one of: a current chroma block, or a collocated luma block. The conversion is performed based on the determining.

Embodiments of the present disclosure provide a solution for video processing. A method for video processing is presented. In the method, for a conversion between a current video block of a video and a bitstream of the video, whether a cross-component prediction (CCP) candidate list mode is applicable to the current video block is determined based on coding and decoding information of at least one of: a current chroma block, or a co-luma block. The conversion is performed based on the determination.

The application discloses a PET reconstruction model construction method based on list data and deep learning, which combines list mode data and deep learning counting through a domain conversion function, and solves the problem that list mode data is difficult to be directly processed through a deep neural network. Through the domain conversion function, the application can convert list mode data of any format and any storage size from a measurement domain to an image domain, so that an end-to-end deep neural network method can be directly used for processing and learning. The application uses deep learning to reconstruct list mode data, and compared with other methods for reconstructing list mode data, the reconstruction speed is greatly improved; for a list mode MLEM algorithm, it takes tens of seconds to reconstruct a brain image, and the application only needs about one second to complete the reconstruction, so that the application has greater application potential in the fields of real-time reconstruction and short-time frame reconstruction.

Systems and methods for estimating mean randomness include: acquiring list pattern data describing true and delayed coincidences detected by a positron emission tomography (PET) scanner during a scan of an object; determining, based on the list pattern data, delayed coincidences including said crystals for each crystal of the PET scanner and for each of a plurality of time periods of the scan; for each crystal, determining a single event rate associated with each time period based on the delayed coincidences determined for the crystal within the time period; for each time period, determining an estimated mean randomness for each of a plurality of crystal pairs based on the single event rate associated with the time period for each crystal of the crystal pair; and reconstructing an image of the object based on the estimated mean randomness for each time period and the detected true coincidences.

A PET apparatus according to one embodiment includes processing circuitry. The processing circuitry acquires list mode data. The processing circuitry reconstructs a PET image based on the list mode data. The processing circuitry identifies first coincidence data that is obtained by coincidence counting by a first detector and a second detector that is different from the first detector from among a plurality of pieces of coincidence data included in the list mode data. The processing circuitry estimates a time lag amount between the first detector and the second detector based on TOF information included in the first coincidence data and the PET image.

A radiation detection device includes a gamma detector configured to receive a stream of non-random and random photon events and generate corresponding list-mode data, and a computing device in communication with the gamma detector configured to receive the list-mode data, determine, by applying at least one statistical timing analysis method to a segment of the list-mode data, data associated with the non-random photon events, predict, based on the data, a next non-random time window, and reject photon events that have arrival times, at the gamma detector, in the predicted non-random time window.

The invention discloses a light equipment management method and device, a computer readable medium and electronic equipment, and the method comprises the steps: responding to a selection operation of a list mode, and displaying a list region, a control region and a preset mode region on a display interface at the same time; wherein the list area is used for displaying description information of light equipment needing to be managed in a list mode; the control area is used for displaying adjustable parameters of the light equipment; the preset mode area is used for displaying a pre-created scene control mode, and one scene control mode comprises at least one lighting device and a preset adjustment parameter of the lighting device. According to the technical scheme, the equipment list, the adjustment parameters, the scene mode and other information are synchronously displayed on the same interface, frequent jumping and switching among different function pages by a user are avoided, the information searching cost is reduced, and therefore the management efficiency of the lamplight equipment is improved.

A positron emission tomography (PET) system is provided. The system includes an image reconstruction computing device. The processors of the image reconstruction computing device are programmed to receive event data acquired by the PET system. The event data are represented as a list of coincidence events. The processors are also programmed to generate groups of coincidence events based on the event data, each group being representative of the event data. The processors are further programmed to perform a first level of image updates by iteratively updating a reconstructed image. Each image update is based on a first number of coincidence events. Further, the processors are programmed to perform a second level of image updates by iteratively updating the reconstructed image. Each image update is based on a second number of coincidence events. The first number of coincidence events is different from the second number of coincidence events.